loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Alfredo Cuzzocrea 1 ; Salvatore Cavalieri 2 ; Orazio Tomarchio 2 ; Giuseppe Di Modica 2 ; Concetta Cantone 3 and Angela Di Bilio 3

Affiliations: 1 DIA Dept., University of Trieste, Trieste and Italy ; 2 DIEEI Dept., University of Catania, Catania and Italy ; 3 Xenia Software Solutions, Catania and Italy

Keyword(s): Big Data Analytics, Data-intensive Business Processes, OLAP-based Big Data Analytics and Complex Architectures and Systems.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Data Warehouses and OLAP ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: In this paper, we provide architecture and functionalities of REMS.PA, a complex framework for supporting OLAP-based big data analytics over data-intensive business processes, with particular regards to business processes of the Public Administration. The framework has been designed and developed in the context of a real-life project. In addition to the anatomy of the framework, we describe some case studies that contribute to highlight the benefits coming from our proposed framework.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.116.42.208

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Cuzzocrea, A.; Cavalieri, S.; Tomarchio, O.; Di Modica, G.; Cantone, C. and Di Bilio, A. (2019). REMS.PA: A Complex Framework for Supporting OLAP-based Big Data Analytics over Data-intensive Business Processes. In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-372-8; ISSN 2184-4984, SciTePress, pages 223-230. DOI: 10.5220/0007737002230230

@conference{iceis19,
author={Alfredo Cuzzocrea. and Salvatore Cavalieri. and Orazio Tomarchio. and Giuseppe {Di Modica}. and Concetta Cantone. and Angela {Di Bilio}.},
title={REMS.PA: A Complex Framework for Supporting OLAP-based Big Data Analytics over Data-intensive Business Processes},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2019},
pages={223-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007737002230230},
isbn={978-989-758-372-8},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - REMS.PA: A Complex Framework for Supporting OLAP-based Big Data Analytics over Data-intensive Business Processes
SN - 978-989-758-372-8
IS - 2184-4984
AU - Cuzzocrea, A.
AU - Cavalieri, S.
AU - Tomarchio, O.
AU - Di Modica, G.
AU - Cantone, C.
AU - Di Bilio, A.
PY - 2019
SP - 223
EP - 230
DO - 10.5220/0007737002230230
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic